Adaptive Data Communication Interface: A User-Centric Visual Data Interpretation Framework

TL;DR

This paper proposes a user-centric, adaptive visual data interpretation framework that leverages AI to improve data visualization and understanding through iterative learning and knowledge base enhancement.

Contribution

It introduces a novel, multi-stage framework integrating AI and knowledge bases for adaptive, personalized data visualization systems.

Findings

Framework design and stages outlined

Discussion of AI integration for visualization

Potential for iterative system improvement

Abstract

In this position paper, we present ideas about creating a next generation framework towards an adaptive interface for data communication and visualisation systems. Our objective is to develop a system that accepts large data sets as inputs and provides user-centric, meaningful visual information to assist owners to make sense of their data collection. The proposed framework comprises four stages: (i) the knowledge base compilation, where we search and collect existing state-ofthe-art visualisation techniques per domain and user preferences; (ii) the development of the learning and inference system, where we apply artificial intelligence techniques to learn, predict and recommend new graphic interpretations (iii) results evaluation; and (iv) reinforcement and adaptation, where valid outputs are stored in our knowledge base and the system is iteratively tuned to address new demands. These stages, as well as our overall vision, limitations and possible challenges are introduced in this article. We also discuss further extensions of this framework for other knowledge discovery tasks.